The results of the six transfer tasks of the ImageCLEF-DA dataset are shown in Table REF . Although the number of images in each subdomain in the ImageCLEF-DA dataset is similar, it is still challenging for the transfer task because of the images from various scenarios. Compared to ResNet-50, which only utilizes source domain samples for fine-tuning, the above-mentioned domain adaptation method achieves significant effect. Compared with other methods, the method in this article has achieved significant improvement.
The CAT method also adopts the idea of semi-supervised learning, but it only adopts the Pi-model [1]} prediction ensembling method, and we utilize model ensembling, which is more effective. This also proves that the student-teacher model of REN can learn more transferable features.
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